{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T16:10:29Z","timestamp":1778429429153,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Laboratory of Uranium Resources Exploration\u2013Mining and Nuclear Remote Sensing, East China University of Technology","award":["2024QZ-TD-10"],"award-info":[{"award-number":["2024QZ-TD-10"]}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["20253BAC260013"],"award-info":[{"award-number":["20253BAC260013"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["2024QZ-TD-10"],"award-info":[{"award-number":["2024QZ-TD-10"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In practical well-logging datasets, severe missing values, anomalous disturbances, and highly imbalanced lithology classes are pervasive. To address these challenges, this study proposes a well-logging lithology identification framework that combines Robust Feature Engineering (RFE) with quality-aware XGBoost. Instead of relying on interpolation-based data cleaning, RFE uses sentinel values and a meta-information tensor to explicitly encode patterns of missingness and anomalies, and incorporates sliding-window context to transform data defects into discriminative auxiliary features. In parallel, a quality-aware sample-weighting strategy is introduced that jointly accounts for formation boundary locations and label confidence, thereby mitigating training bias induced by long-tailed class distributions. Experiments on the FORCE 2020 lithology prediction dataset demonstrate that, relative to baseline models, the proposed method improves the weighted F1 score from 0.66 to 0.73, while Boundary F1 and the geological penalty score are also consistently enhanced. These results indicate that, compared with traditional workflows that rely solely on data cleaning, explicit modeling of data incompleteness provides more pronounced advantages in terms of robustness and engineering applicability.<\/jats:p>","DOI":"10.3390\/bdcc10020047","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:12:46Z","timestamp":1770041566000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lithology Identification from Well Logs via Meta-Information Tensors and Quality-Aware Weighting"],"prefix":"10.3390","volume":"10","author":[{"given":"Wenxuan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Software, East China University of Technology, Nanchang 330013, China"},{"name":"Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China"},{"name":"School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6019-1155","authenticated-orcid":false,"given":"Fan","family":"Diao","sequence":"additional","affiliation":[{"name":"Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China"},{"name":"School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5075-3279","authenticated-orcid":false,"given":"Peng","family":"Ding","sequence":"additional","affiliation":[{"name":"Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China"},{"name":"School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"He","sequence":"additional","affiliation":[{"name":"Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China"},{"name":"School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1016\/j.petsci.2025.03.023","article-title":"Extracting Useful Information from Sparsely Logged Wellbores for Improved Rock Typing of Heterogeneous Reservoir Characterization Using Well-Log Attributes, Feature Influence and Optimization","volume":"22","author":"Wood","year":"2025","journal-title":"Pet. 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